Publication in BibTeX Format

@TECHREPORT{AICPub587:1985,
AUTHOR={Georgeff, Michael P. and Wallace, Christopher S.},
TITLE={A General Selection Criterion For Inductive Inference},
ADDRESS={333 Ravenswood Ave., Menlo Park, CA 94025},
INSTITUTION={AI Center, SRI International},
MONTH={Dec},
NUMBER={372},
YEAR={1985},
KEYWORDS={Reasoning!Induction, Reasoning!Abduction},
ABSTRACT={This paper presents a general criterion for measuring the degree to
which any given theory can be considered a good explanation of a particular
body of data. A formal definition of what constitutes an acceptable explanation
of a body of data is given, and the length of explanation used as a measure
for selecting the best of a set of competing theories. Unlike most previous
approaches to inductive inference, the length of explanation includes a measure
of the complexity or likelihood of a theory as well as a measure of the degree
of fit between theory and data. In this way, prior expectations about the environment
can be represented, thus providing a hypothesis space in which search for good
or optimal theories is made more tractable. Furthermore, it is shown how theories
can be represented as structures that reflect the conceptual entities used
to describe and reason about the given problem domain.}
}